clean imports and delete "create_XXX_functions"

algorithms/neat/function_factory.py

@@ -6,11 +6,7 @@ from functools import partial
 import numpy as np
 from jax import jit, vmap
 
-from .genome import act_name2key, agg_name2key
+from .genome import act_name2key, agg_name2key, initialize_genomes, mutate, distance, crossover
-from .genome.genome import initialize_genomes
-from .genome.mutate import mutate
-from .genome.distance import distance
-from .genome.crossover import crossover
 
 
 class FunctionFactory:

algorithms/neat/genome/__init__.py

@@ -1,7 +1,7 @@
-from .genome import create_initialize_function, expand, expand_single, pop_analysis
+from .genome import expand, expand_single, pop_analysis, initialize_genomes
-from .distance import create_distance_function
-from .mutate import create_mutate_function
 from .forward import create_forward_function
-from .crossover import create_crossover_function
 from .activations import act_name2key
 from .aggregations import agg_name2key
+from .crossover import crossover
+from .mutate import mutate
+from .distance import distance

algorithms/neat/genome/crossover.py

@@ -8,38 +8,7 @@ from jax import numpy as jnp
 from .utils import flatten_connections, unflatten_connections
 
 
-def create_crossover_function(N, config, batch: bool, debug: bool = False):
+@jit
-    if batch:
-        pop_size = config.neat.population.pop_size
-        randkey_lower = jnp.zeros((pop_size, 2), dtype=jnp.uint32)
-        nodes1_lower = jnp.zeros((pop_size, N, 5))
-        connections1_lower = jnp.zeros((pop_size, 2, N, N))
-        nodes2_lower = jnp.zeros((pop_size, N, 5))
-        connections2_lower = jnp.zeros((pop_size, 2, N, N))
-
-        res_func = jit(vmap(crossover)).lower(randkey_lower, nodes1_lower, connections1_lower,
-                                              nodes2_lower, connections2_lower).compile()
-        if debug:
-            return lambda *args: res_func(*args)
-        else:
-            return res_func
-
-    else:
-        randkey_lower = jnp.zeros((2,), dtype=jnp.uint32)
-        nodes1_lower = jnp.zeros((N, 5))
-        connections1_lower = jnp.zeros((2, N, N))
-        nodes2_lower = jnp.zeros((N, 5))
-        connections2_lower = jnp.zeros((2, N, N))
-
-        res_func = jit(crossover).lower(randkey_lower, nodes1_lower, connections1_lower,
-                                        nodes2_lower, connections2_lower).compile()
-        if debug:
-            return lambda *args: res_func(*args)
-        else:
-            return res_func
-
-
-# @jit
 def crossover(randkey: Array, nodes1: Array, connections1: Array, nodes2: Array, connections2: Array) \
         -> Tuple[Array, Array]:
     """
@@ -70,7 +39,7 @@ def crossover(randkey: Array, nodes1: Array, connections1: Array, nodes2: Array,
     return new_nodes, new_cons
 
 
-# @partial(jit, static_argnames=['gene_type'])
+@partial(jit, static_argnames=['gene_type'])
 def align_array(seq1: Array, seq2: Array, ar2: Array, gene_type: str) -> Array:
     """
     make ar2 align with ar1.
@@ -97,7 +66,7 @@ def align_array(seq1: Array, seq2: Array, ar2: Array, gene_type: str) -> Array:
     return refactor_ar2
 
 
-# @jit
+@jit
 def crossover_gene(rand_key: Array, g1: Array, g2: Array) -> Array:
     """
     crossover two genes

algorithms/neat/genome/distance.py

@@ -1,139 +1,9 @@
 from jax import jit, vmap, Array
 from jax import numpy as jnp
-import numpy as np
-from numpy.typing import NDArray
 
 from .utils import flatten_connections, EMPTY_NODE, EMPTY_CON
 
 
-def create_distance_function(N, config, type: str, debug: bool = False):
-    """
-    :param N:
-    :param config:
-    :param type: {'o2o', 'o2m'}, for one-to-one or one-to-many distance calculation
-    :param debug:
-    :return:
-    """
-    disjoint_coe = config.neat.genome.compatibility_disjoint_coefficient
-    compatibility_coe = config.neat.genome.compatibility_weight_coefficient
-
-    def distance_with_args(nodes1, connections1, nodes2, connections2):
-        return distance(nodes1, connections1, nodes2, connections2, disjoint_coe, compatibility_coe)
-
-    if type == 'o2o':
-        nodes1_lower = jnp.zeros((N, 5))
-        connections1_lower = jnp.zeros((2, N, N))
-        nodes2_lower = jnp.zeros((N, 5))
-        connections2_lower = jnp.zeros((2, N, N))
-
-        res_func = jit(distance_with_args).lower(nodes1_lower, connections1_lower,
-                                                 nodes2_lower, connections2_lower).compile()
-        if debug:
-            return lambda *args: res_func(*args)  # for debug
-        else:
-            return res_func
-
-    elif type == 'o2m':
-        vmap_func = vmap(distance_with_args, in_axes=(None, None, 0, 0))
-        pop_size = config.neat.population.pop_size
-        nodes1_lower = jnp.zeros((N, 5))
-        connections1_lower = jnp.zeros((2, N, N))
-        nodes2_lower = jnp.zeros((pop_size, N, 5))
-        connections2_lower = jnp.zeros((pop_size, 2, N, N))
-        res_func = jit(vmap_func).lower(nodes1_lower, connections1_lower, nodes2_lower, connections2_lower).compile()
-        if debug:
-            return lambda *args: res_func(*args)  # for debug
-        else:
-            return res_func
-
-    else:
-        raise ValueError(f'unknown distance type: {type}, should be one of ["o2o", "o2m"]')
-
-
-def distance_numpy(nodes1: NDArray, connection1: NDArray, nodes2: NDArray,
-                   connection2: NDArray, disjoint_coe: float = 1., compatibility_coe: float = 0.5):
-    """
-    use in o2o distance.
-    o2o can't use vmap, numpy should be faster than jax function
-    :param nodes1:
-    :param connection1:
-    :param nodes2:
-    :param connection2:
-    :param disjoint_coe:
-    :param compatibility_coe:
-    :return:
-    """
-
-    def analysis(nodes, connections):
-        nodes_dict = {}
-        idx2key = {}
-        for i, node in enumerate(nodes):
-            if np.isnan(node[0]):
-                continue
-            key = int(node[0])
-            nodes_dict[key] = (node[1], node[2], node[3], node[4])
-            idx2key[i] = key
-
-        connections_dict = {}
-        for i in range(connections.shape[1]):
-            for j in range(connections.shape[2]):
-                if np.isnan(connections[0, i, j]) and np.isnan(connections[1, i, j]):
-                    continue
-                key = (idx2key[i], idx2key[j])
-
-                weight = connections[0, i, j] if not np.isnan(connections[0, i, j]) else None
-                enabled = (connections[1, i, j] == 1) if not np.isnan(connections[1, i, j]) else None
-                connections_dict[key] = (weight, enabled)
-
-        return nodes_dict, connections_dict
-
-    nodes1, connections1 = analysis(nodes1, connection1)
-    nodes2, connections2 = analysis(nodes2, connection2)
-
-    nd = 0.0
-    if nodes1 or nodes2:  # otherwise, both are empty
-        disjoint_nodes = 0
-        for k2 in nodes2:
-            if k2 not in nodes1:
-                disjoint_nodes += 1
-
-        for k1, n1 in nodes1.items():
-            n2 = nodes2.get(k1)
-            if n2 is None:
-                disjoint_nodes += 1
-            else:
-                if np.isnan(n1[0]):  # n1[1] is nan means input nodes
-                    continue
-                d = abs(n1[0] - n2[0]) + abs(n1[1] - n2[1])
-                d += 1 if n1[2] != n2[2] else 0
-                d += 1 if n1[3] != n2[3] else 0
-                nd += d
-
-        max_nodes = max(len(nodes1), len(nodes2))
-        nd = (compatibility_coe * nd + disjoint_coe * disjoint_nodes) / max_nodes
-
-    cd = 0.0
-    if connections1 or connections2:
-        disjoint_connections = 0
-        for k2 in connections2:
-            if k2 not in connections1:
-                disjoint_connections += 1
-
-        for k1, c1 in connections1.items():
-            c2 = connections2.get(k1)
-            if c2 is None:
-                disjoint_connections += 1
-            else:
-                # Homologous genes compute their own distance value.
-                d = abs(c1[0] - c2[0])
-                d += 1 if c1[1] != c2[1] else 0
-                cd += d
-        max_conn = max(len(connections1), len(connections2))
-        cd = (compatibility_coe * cd + disjoint_coe * disjoint_connections) / max_conn
-
-    return nd + cd
-
-
 @jit
 def distance(nodes1: Array, connections1: Array, nodes2: Array, connections2: Array, disjoint_coe: float = 1.,
              compatibility_coe: float = 0.5) -> Array:

algorithms/neat/genome/genome.py

@@ -22,27 +22,11 @@ from jax import numpy as jnp
 from jax import jit
 from jax import Array
 
-from .activations import act_name2key
-from .aggregations import agg_name2key
 from .utils import fetch_first
 
 EMPTY_NODE = np.array([np.nan, np.nan, np.nan, np.nan, np.nan])
 
 
-def create_initialize_function(config):
-    pop_size = config.neat.population.pop_size
-    N = config.basic.init_maximum_nodes
-    num_inputs = config.basic.num_inputs
-    num_outputs = config.basic.num_outputs
-    default_bias = config.neat.gene.bias.init_mean
-    default_response = config.neat.gene.response.init_mean
-    default_act = act_name2key[config.neat.gene.activation.default]
-    default_agg = agg_name2key[config.neat.gene.aggregation.default]
-    default_weight = config.neat.gene.weight.init_mean
-    return partial(initialize_genomes, pop_size, N, num_inputs, num_outputs, default_bias, default_response,
-                   default_act, default_agg, default_weight)
-
-
 def initialize_genomes(pop_size: int,
                        N: int,
                        num_inputs: int,

algorithms/neat/genome/mutate.py

@@ -13,100 +13,7 @@ from .activations import act_name2key
 from .aggregations import agg_name2key
 
 
-def create_mutate_function(N, config, batch: bool, debug: bool = False):
+@partial(jit, static_argnames=('single_structure_mutate',))
-    """
-    create mutate function for different situations
-    :param N:
-    :param config:
-    :param batch: mutate for population or not
-    :param debug:
-    :return:
-    """
-    num_inputs = config.basic.num_inputs
-    num_outputs = config.basic.num_outputs
-    input_idx = np.arange(num_inputs)
-    output_idx = np.arange(num_inputs, num_inputs + num_outputs)
-
-    bias = config.neat.gene.bias
-    bias_default = bias.init_mean
-    bias_mean = bias.init_mean
-    bias_std = bias.init_stdev
-    bias_mutate_strength = bias.mutate_power
-    bias_mutate_rate = bias.mutate_rate
-    bias_replace_rate = bias.replace_rate
-
-    response = config.neat.gene.response
-    response_default = response.init_mean
-    response_mean = response.init_mean
-    response_std = response.init_stdev
-    response_mutate_strength = response.mutate_power
-    response_mutate_rate = response.mutate_rate
-    response_replace_rate = response.replace_rate
-
-    weight = config.neat.gene.weight
-    weight_mean = weight.init_mean
-    weight_std = weight.init_stdev
-    weight_mutate_strength = weight.mutate_power
-    weight_mutate_rate = weight.mutate_rate
-    weight_replace_rate = weight.replace_rate
-
-    activation = config.neat.gene.activation
-    act_default = act_name2key[activation.default]
-    act_list = np.array([act_name2key[name] for name in activation.options])
-    act_replace_rate = activation.mutate_rate
-
-    aggregation = config.neat.gene.aggregation
-    agg_default = agg_name2key[aggregation.default]
-    agg_list = np.array([agg_name2key[name] for name in aggregation.options])
-    agg_replace_rate = aggregation.mutate_rate
-
-    enabled = config.neat.gene.enabled
-    enabled_reverse_rate = enabled.mutate_rate
-
-    genome = config.neat.genome
-    add_node_rate = genome.node_add_prob
-    delete_node_rate = genome.node_delete_prob
-    add_connection_rate = genome.conn_add_prob
-    delete_connection_rate = genome.conn_delete_prob
-    single_structure_mutate = genome.single_structural_mutation
-
-    def mutate_with_args(rand_key, nodes, connections, new_node_key):
-        return mutate(rand_key, nodes, connections, new_node_key, input_idx, output_idx,
-                      bias_default, bias_mean, bias_std, bias_mutate_strength, bias_mutate_rate,
-                      bias_replace_rate, response_default, response_mean, response_std,
-                      response_mutate_strength, response_mutate_rate, response_replace_rate,
-                      weight_mean, weight_std, weight_mutate_strength, weight_mutate_rate,
-                      weight_replace_rate, act_default, act_list, act_replace_rate,
-                      agg_default, agg_list, agg_replace_rate, enabled_reverse_rate,
-                      add_node_rate, delete_node_rate, add_connection_rate, delete_connection_rate,
-                      single_structure_mutate)
-
-    if not batch:
-        rand_key_lower = jnp.zeros((2,), dtype=jnp.uint32)
-        nodes_lower = jnp.zeros((N, 5))
-        connections_lower = jnp.zeros((2, N, N))
-        new_node_key_lower = jnp.zeros((), dtype=jnp.int32)
-        res_func = jit(mutate_with_args).lower(rand_key_lower, nodes_lower,
-                                               connections_lower, new_node_key_lower).compile()
-        if debug:
-            return lambda *args: res_func(*args)
-        else:
-            return res_func
-    else:
-        pop_size = config.neat.population.pop_size
-        rand_key_lower = jnp.zeros((pop_size, 2), dtype=jnp.uint32)
-        nodes_lower = jnp.zeros((pop_size, N, 5))
-        connections_lower = jnp.zeros((pop_size, 2, N, N))
-        new_node_key_lower = jnp.zeros((pop_size,), dtype=jnp.int32)
-
-        batched_mutate_func = jit(vmap(mutate_with_args)).lower(rand_key_lower, nodes_lower,
-                                                                connections_lower, new_node_key_lower).compile()
-        if debug:
-            return lambda *args: batched_mutate_func(*args)
-        else:
-            return batched_mutate_func
-
-
 def mutate(rand_key: Array,
            nodes: Array,
            connections: Array,
@@ -243,6 +150,7 @@ def mutate(rand_key: Array,
     return nodes, connections
 
 
+@jit
 def mutate_values(rand_key: Array,
                   nodes: Array,
                   connections: Array,
@@ -323,6 +231,7 @@ def mutate_values(rand_key: Array,
     return nodes, connections
 
 
+@jit
 def mutate_float_values(rand_key: Array, old_vals: Array, mean: float, std: float,
                         mutate_strength: float, mutate_rate: float, replace_rate: float) -> Array:
     """
@@ -355,6 +264,7 @@ def mutate_float_values(rand_key: Array, old_vals: Array, mean: float, std: floa
     return new_vals
 
 
+@jit
 def mutate_int_values(rand_key: Array, old_vals: Array, val_list: Array, replace_rate: float) -> Array:
     """
     Mutate integer values (act, agg) of a given array.
@@ -377,6 +287,7 @@ def mutate_int_values(rand_key: Array, old_vals: Array, val_list: Array, replace
     return new_vals
 
 
+@jit
 def mutate_add_node(rand_key: Array, new_node_key: int, nodes: Array, connections: Array,
                     default_bias: float = 0, default_response: float = 1,
                     default_act: int = 0, default_agg: int = 0) -> Tuple[Array, Array]:
@@ -423,6 +334,7 @@ def mutate_add_node(rand_key: Array, new_node_key: int, nodes: Array, connection
     return nodes, connections
 
 
+@jit
 def mutate_delete_node(rand_key: Array, nodes: Array, connections: Array,
                        input_keys: Array, output_keys: Array) -> Tuple[Array, Array]:
     """
@@ -456,6 +368,7 @@ def mutate_delete_node(rand_key: Array, nodes: Array, connections: Array,
     return nodes, connections
 
 
+@jit
 def mutate_add_connection(rand_key: Array, nodes: Array, connections: Array,
                           input_keys: Array, output_keys: Array) -> Tuple[Array, Array]:
     """
@@ -494,6 +407,7 @@ def mutate_add_connection(rand_key: Array, nodes: Array, connections: Array,
     return nodes, connections
 
 
+@jit
 def mutate_delete_connection(rand_key: Array, nodes: Array, connections: Array):
     """
     Randomly delete a connection.
@@ -516,6 +430,7 @@ def mutate_delete_connection(rand_key: Array, nodes: Array, connections: Array):
     return nodes, connections
 
 
+@partial(jit, static_argnames=('allow_input_keys', 'allow_output_keys'))
 def choice_node_key(rand_key: Array, nodes: Array,
                     input_keys: Array, output_keys: Array,
                     allow_input_keys: bool = False, allow_output_keys: bool = False) -> Tuple[Array, Array]:
@@ -544,6 +459,7 @@ def choice_node_key(rand_key: Array, nodes: Array,
     return key, idx
 
 
+@jit
 def choice_connection_key(rand_key: Array, nodes: Array, connection: Array) -> Tuple[Array, Array, Array, Array]:
     """
     Randomly choose a connection key from the given connections.
@@ -571,5 +487,6 @@ def choice_connection_key(rand_key: Array, nodes: Array, connection: Array) -> T
     return from_key, to_key, from_idx, to_idx
 
 
+@jit
 def rand(rand_key):
     return jax.random.uniform(rand_key, ())

algorithms/neat/pipeline.py

@@ -5,9 +5,7 @@ import jax
 import numpy as np
 
 from .species import SpeciesController
-from .genome import expand, expand_single
+from .genome import expand, expand_single, create_forward_function
-from .genome import create_initialize_function, create_mutate_function, create_forward_function, \
-    create_distance_function, create_crossover_function
 from .function_factory import FunctionFactory